FEIT Research Project Database

Efficient and effective adaptive radiotherapy

Project Leader: Leigh Johnston
Collaborators: Nick Hardcastle (Peter MacCallum Cancer Foundation), James Korte (Peter MacCallum Cancer Foundation), Price Jackson (Peter MacCallum Cancer Foundation)
Primary Contact: Leigh Johnston (l.johnston@unimelb.edu.au)
Keywords: cancer; imaging; machine learning; medical image analysis
Disciplines: Biomedical Engineering

Approximately 40% of cancer patients benefit from radiotherapy. Radiotherapy involves highly targeted radiation beams directed at the tumour, whilst avoiding surrounding normal tissues. Anatomical variations introduce targeting uncertainty in the process. Adaptive radiotherapy incorporates medical imaging at multiple time points during radiotherapy delivery with adaptation of treatment geometry to allow improved targeting of the radiation. This project involves application of advanced image analysis and reconstruction to efficiently identify tumour and other anatomy in medical images, compute potential benefits of radiotherapy adaptation on a per-patient basis and generation of new radiotherapy treatment planning geometry.

This project would suit a masters or PhD student with a strong background in image analysis and or machine learning. Study results will directly improve our ability to target radiotherapy treatments and will have directly translatable outcomes into the clinic.

Further information: Please contact Dr James Korte (James.Korte@petermac.org) for further information about this project.